| Image Denoising Using Local Adaptive Least Squares Support Vector Regression |
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| 作者姓名： | WU Dingxue PENG Daiqiang TIAN Jinwen |
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| 作者单位： | Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology，1037 LuoyuRoad Wuhan 430074 China |
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| 基金项目： | Supported by the Foundation of Hubei Provincial Department of Education(No.2003EB0018). |
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| 摘 要： | Rather than attempting to separate signal from noise in the spatial domain, it is often advanta- geous to work in a transform domain. Building on previous work, a novel denoising method based on local adaptive least squares support vector regression is proposed. Investigation on real images contaminated by Gaussian noise has demonstrated that the proposed method can achieve an acceptable trade off between the noise removal and smoothing of the edges and details.
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| 关 键 词： | 图象降噪 最小平方支持向量机 分割信号 衰退 |
| 文章编号： | 1009-5020（2007）03-196-04 |
| 收稿时间： | 22 June 2007 |
| 修稿时间： | 2007-06-22 |
Image denoising using local adaptive least squares support vector regression |
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| WU Dingxue PENG Daiqiang TIAN Jinwen.Image Denoising Using Local Adaptive Least Squares Support Vector Regression[J].Geo-Spatial Information Science,2007,10(3):196-199. |
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| Authors: | Wu Dingxue Peng Daiqiang Tian Jinwen |
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| Institution: | (1) Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, China |
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| Abstract: | Rather than attempting to separate signal from noise in the spatial domain, it is often advanta- geous to work in a transform domain. Building on previous work, a novel denoising method based on local adaptive least squares support vector regression is proposed. Investigation on real images contaminated by Gaussian noise has demonstrated that the proposed method can achieve an acceptable trade off between the noise removal and smoothing of the edges and details. |
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| Keywords: | least square support vector machines image denoising |
| 本文献已被 CNKI 维普 SpringerLink 等数据库收录！ |
